1. Field of the Invention
This invention relates generally to the field of customer self service systems for resource search and selection, and more specifically, to a novel mechanism for providing a response set based on user queries and derived user contexts and that is adaptable for modifying output response sets in accordance with different user contexts and user interactions as they change over time.
2. Discussion of the Prior Art
Currently there exist many systems designed to perform search and retrieval functions.
These systems may be classified variously as knowledge management systems, information portals, search engines, data miners, etc. However, providing effective customer self service systems for resource search and selection presents several significant challenges. The first challenge for current systems with query capability is that serving queries intelligently requires a large amount of user supplied contextual information, while at the same time the user has limited time, patience, ability and interest to provide it. The second challenge is that searching without sufficient context results in a very inefficient search (both user time and system resource intensive) with frequently disappointing results (overwhelming amount of information, high percentage of irrelevant information). The third challenge is that much of a user""s actual use and satisfaction with search results differ from that defined at the start of the search: either because the users behave contrary to their own specifications, or because there are other contextual issues at play that have not been defined into the search.
The prior art has separately addressed the use of the history of interaction with the user or their current service environment to provide context for building a resource response set. The prior art also assumes the shallow context of a single user query stream focused on a single topic.
As will be hereinafter explained in greater detail, some representative prior art search and retrieval systems include Feldman, Susan, xe2x80x9cThe Answering Machine,xe2x80x9d in Searcher: The Magazine for Database Professionals, 1, 8, Jan, 2000/58; U.S. Pat. No. 5,974,412 entitled xe2x80x9cIntelligent Query System for Automatically Indexing Information in a Database and Automatically Categorizing Usersxe2x80x9d; U.S. Pat. No. 5,600, 835 entitled xe2x80x9cAdaptive Non-Literal Text String Retrievalxe2x80x9d; U.S. Pat. No. 6,105,023 entitled xe2x80x9cSystem and Method for Filtering a Document Streamxe2x80x9d; and, U.S. Pat. No. 5,754,939 entitled xe2x80x9cSystem for Generation of User Profiles For a System For Customized Electronic Identification of Desirable Objects.xe2x80x9d
For example, the article by Feldman, Susan entitled xe2x80x9cThe Answer Machine,xe2x80x9d discusses generally how the use of learning may make systems dynamic, however, the systems related to learning appear to be focused on learning a taxonomy or relationships among document categories or topics. Such learning systems may detect the rise of new terms. For example, the Semio system (http://www.semio.com/products/semiotaxonomy.html) creates taxonomies or hierarchies automatically. However, none of the systems for learning in the prior art are focused on or uses user contexts. Moreover, no system in the prior art is directed to discovering clusters in user behaviors (user context clusters).
U.S. Pat. No. 5,974,412 describes an adaptive retrieval system that uses a vector of document and query features to drive the retrieval process. Specifically described is an Intelligent Query Engine (IQE) system that develops multiple information spaces in which different types of real-world objects (e.g., documents, users, products) can be represented. Machine learning techniques are used to facilitate automated emergence of information spaces in which objects are represented as vectors of real numbers. The system then delivers information to users based upon similarity measures applied to the representation of the objects in these information spaces. The system simultaneously classifies documents, users, products, and other objects with documents managed by collators that act as classifiers of overlapping portions of the database of documents. Collators evolve to meet the demands for information delivery expressed by user feedback. Liaisons act on the behalf of users to elicit information from the population of collators. This information is then presented to users upon logging into the system via Internet or another communication channel.
U.S. Pat. No. 5,600,835 describes a method and system for selectively retrieving information contained in a stored document set using a non-literal, or xe2x80x9cfuzzyxe2x80x9d, search strategy, and particularly implements an adaptive retrieval approach. A text string query is transmitted to a computer processor, and a dissimilarity value Di is assigned to selected ones of stored text strings representative of information contained in a stored document set, based upon a first set of rules. A set of retrieved text strings representative of stored information and related to the text string query is generated, based upon a second set of rules. Each of the retrieved text strings has an associated dissimilarity value Di, which is a function of at least one rule Rn from the first set of rules used to retrieve the text string and a weight value wn associated with that rule Rn. The retrieved text strings are displayed preferably in an order based on their associated dissimilarity value Di. Once one or more of the retrieved text strings is chosen, the weight value wn associated with at least one rule of the first set of rules is adjusted and stored.
U.S. Pat. No. 6,105,023 entitled xe2x80x9cSystem and Method for Filtering A Document Streamxe2x80x9d is directed to a robust document retrieval system, albeit it is not adaptive. Particularly, it describes a method for filtering incoming documents that includes the steps of receiving an incoming document and parsing it to produce an inverted list of terms contained in the incoming document. The inverted list is then used to retrieve user queries. Any user queries matching less than a pre-determined number of terms are immediately discarded. The remaining user queries are scored and user queries having a score less than a predetermined threshold are discarded. The remaining user queries are the queries which the incoming document matches.
U.S. Pat. No. 5,754,939 describes a method for customized electronic identification of desirable objects, such as news articles, in an electronic media environment, and in particular to a system that automatically constructs both a xe2x80x9ctarget profilexe2x80x9d for each target object in the electronic media based, for example, on the frequency with which each word appears in an article relative to its overall frequency of use in all articles, as well as a xe2x80x9ctarget profile interest summaryxe2x80x9d for each user, which target profile interest summary describes the user""s interest level in various types of target objects. The system then evaluates the target profiles against the users"" target profile interest summaries to generate a user-customized rank ordered listing of target objects most likely to be of interest to each user so that the user can select from among these potentially relevant target objects, which were automatically selected by this system from the plethora of target objects that are profiled on the electronic media.
A major limitation of these prior art approaches however, is their inability to apply specific user context to improve resource selection for other users on the same topic and their inability to adaptively respond to the same search query by the same user over time based on changes in user context and the user""s history of prior interaction with the resource search and selection system. These approaches are also limited in their ability to dynamically generate inclusionary and exclusionary content filters as a bi-product of building the response set.
By returning the same response set to the same query regardless of the user""s current context and previous selections, current self service search and selection systems include many choices of limited relevance and usefulness to users.
It would be highly desirable to provide for a customer self service system, a mechanism that provides a response set based on user queries and derived user contexts that is adaptable for modifying output response sets in accordance with different user contexts and user interactions as they change over time.
It is an object of the present invention to provide for a customer self service system for resource search and selection a mechanism that provides a response set based on user queries and derived user contexts that is adaptable for modifying output response sets in accordance with different user contexts and user interactions as they change over time.
It is another object of the present invention to provide an adaptive indexing function for a customer self service system for resource search and selection that implements a supervised learning algorithm to produce a resource response set based on a user query.
According to the invention, there is provided a system and method for Adaptive Indexing and lookup for a customer self service system that performs resource search and selection and includes a resource library having selectable resources. The method includes steps of: receiving a current user query for requesting resources; receiving a user context vector associated with the current user query, the user context vector comprising data associating an interaction state with the user; mapping each user query and associated context vector to a sub-set of resources from the resource library; and, generating a response set including the sub-set of resources that are most relevant to the user""s query.
In an off-line process, an adaptive indexing function is applied for increasing the value of search results for a current user in their context, the adaptive indexing function enhancing the resource indexing functions by increasing their relevance and specificity for mapping user queries to resources. The adaptive indexing process implements a supervised learning algorithm for receiving user interaction data from among a database of user interaction records and resources from the resource library, and adapts resource indexing functions based on a history of user interactions and user feedback with the system as provided in user interaction records. In this manner the supervised learning algorithm optimizes performance of the resource indexing functions as measured by an evaluation metric applied to the user interaction feedback. Feedback from previous user interaction provides data on the success or failure of any particular retrieval. (According to one possible evaluation metric, a particular retrieval is viewed as successful, if the user selects that particular resource from the list of displayed resources.) The adaptive indexing algorithm attempts to optimize the indexing function to maximize the number of successful retrievals.
The result of this invention is the ability to improve a set of resource indexing functions without the need for the user to explicitly train the system, i.e., enables an adaptive response to the same search query over time based on changes in user context and their history of prior interaction with the resource search and selection system.
Advantageously, such a system and method of the invention is applicable for a customer self service system in a variety of customer self service domains including education, real estate and travel.